Hierarchical Exploration and Visualization of Large and Complex Multi-Dimensional Data Sets

大型复杂多维数据集的分层探索与可视化

• 批准号: 9503829
• 负责人: Jane Wilhelms
• 金额: $ 38.89万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-15 至 2001-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9503829&HistoricalAwards=false
• 关键词: Hierarchical; Exploration; Visualization; Large; Complex

This project investigates the use of hierarchical methods for accessing and visualizing large and complex multi- dimensional data sets. Visualization can provide an efficient and powerful means of exploring such data However, most visualization methods are developed for moderately sized non-hierarchial data sets that can be imaged acceptably fast on graphics workstations. In practice, there are many data sets that cannot be effectively explored using these methods. The project is developing and implementing visualization approches designed for very large, multi-dimensional data sets over a variety of grid types. Such data sets present significant problems in the management of time and space resources, which will be addressed by this research. The unifying theme will be the use of hierarchical data structures to manage both resources effectively. Addressing time efficiency, techniques are being developed for the selective traversal and rapid interactive imaging of the data. For space efficiency, various data models, such as wavelets, are investigated to achieve compression, while being directly amendable to visualization. These hierarchies are being integrated with existing visualization techniques for isosurface extraction, direct volume rendering, and flow visualization, many of which have already been developed at UC Santa Cruz and elsewhere for non-hierarchial data. Techniques for rectilinear, structured, and unstructured grids are studied. Parallelization of these methods of SIMD and MIMD machines is also is being explored. This research is developing new algorithms for visualization of large, complex data sets, as well as making software systems available for use by scientists. Software is modular to facilitate integration with existing visualization packages. While the main objective is visualization, the hierarchical methods developed will also be of use for any data analysis hampered by access problems in dealing with very large data sets.

该项目研究了使用分层方法来访问和可视化大型且复杂的多维数据集。 可视化可以提供一种有效且强大的方法来探索此类数据。但是，大多数可视化方法都是针对中等大小的非层次数据集开发的，这些数据集可以在图形工作站上以可接受的速度快速成像。在实践中，有许多数据集无法使用这些方法进行有效探索。 该项目正在开发和实施专为各种网格类型上的超大型多维数据集而设计的可视化方法。 此类数据集在时间和空间资源的管理中提出了重大问题，本研究将解决这些问题。 统一的主题将是使用分层数据结构来有效地管理这两种资源。 为了解决时间效率问题，正在开发用于数据的选择性遍历和快速交互式成像的技术。 为了空间效率，研究了各种数据模型（例如小波）以实现压缩，同时可直接修改为可视化。 这些层次结构正在与用于等值面提取、直接体积渲染和流可视化的现有可视化技术集成，其中许多技术已经在加州大学圣克鲁斯分校和其他地方针对非层次结构数据开发。 研究了直线、结构化和非结构化网格的技术。 SIMD 和 MIMD 机器的这些方法的并行化也正在探索中。 这项研究正在开发用于大型复杂数据集可视化的新算法，并使软件系统可供科学家使用。 软件是模块化的，以便于与现有可视化软件包集成。 虽然主要目标是可视化，但开发的分层方法也可用于处理非常大的数据集时因访问问题而受到阻碍的任何数据分析。